Covariate Adjustment in Bayesian Adaptive Clinical Trials

In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands, and show how to obtain posterior samples of marginal estimands from adjusted analyses. We describe several estimands for three common outcome types. We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.Comment: 17 pages, 5 tables, 4 figure

Double robust estimation of optimal partially adaptive treatment strategies : an application to breast cancer treatment using hormonal therapy

Precision medicine aims to tailor treatment decisions according to patients' characteristics. G-estimation and dynamic weighted ordinary least squares are double robust methods to identify optimal adaptive treatment strategies. It is underappreciated that they require modeling all existing treatment-confounder interactions to be consistent. Identifying optimal partially adaptive treatment strategies that tailor treatments according to only a few covariates, ignoring some interactions, may be preferable in practice. Building on G-estimation and dWOLS, we propose estimators of such partially adaptive strategies and demonstrate their double robustness. We investigate these estimators in a simulation study. Using data maintained by the Centre des Maladies du Sein, we estimate a partially adaptive treatment strategy for tailoring hormonal therapy use in breast cancer patients. R software implementing our estimators is provided

Variable Selection for Individualized Treatment Rules with Discrete Outcomes

An individualized treatment rule (ITR) is a decision rule that aims to improve individual patients health outcomes by recommending optimal treatments according to patients specific information. In observational studies, collected data may contain many variables that are irrelevant for making treatment decisions. Including all available variables in the statistical model for the ITR could yield a loss of efficiency and an unnecessarily complicated treatment rule, which is difficult for physicians to interpret or implement. Thus, a data-driven approach to select important tailoring variables with the aim of improving the estimated decision rules is crucial. While there is a growing body of literature on selecting variables in ITRs with continuous outcomes, relatively few methods exist for discrete outcomes, which pose additional computational challenges even in the absence of variable selection. In this paper, we propose a variable selection method for ITRs with discrete outcomes. We show theoretically and empirically that our approach has the double robustness property, and that it compares favorably with other competing approaches. We illustrate the proposed method on data from a study of an adaptive web-based stress management tool to identify which variables are relevant for tailoring treatment

Effect of breastfeeding on gastrointestinal infection in infants: A targeted maximum likelihood approach for clustered longitudinal data

The PROmotion of Breastfeeding Intervention Trial (PROBIT) cluster-randomized a program encouraging breastfeeding to new mothers in hospital centers. The original studies indicated that this intervention successfully increased duration of breastfeeding and lowered rates of gastrointestinal tract infections in newborns. Additional scientific and popular interest lies in determining the causal effect of longer breastfeeding on gastrointestinal infection. In this study, we estimate the expected infection count under various lengths of breastfeeding in order to estimate the effect of breastfeeding duration on infection. Due to the presence of baseline and time-dependent confounding, specialized "causal" estimation methods are required. We demonstrate the double-robust method of Targeted Maximum Likelihood Estimation (TMLE) in the context of this application and review some related methods and the adjustments required to account for clustering. We compare TMLE (implemented both parametrically and using a data-adaptive algorithm) to other causal methods for this example. In addition, we conduct a simulation study to determine (1) the effectiveness of controlling for clustering indicators when cluster-specific confounders are unmeasured and (2) the importance of using data-adaptive TMLE

Characterizing patterns in police stops by race in Minneapolis from 2016-2021

The murder of George Floyd centered Minneapolis, Minnesota, in conversations on racial injustice in the US. We leverage open data from the Minneapolis Police Department to analyze individual, geographic, and temporal patterns in more than 170,000 police stops since 2016. We evaluate person and vehicle searches at the individual level by race using generalized estimating equations with neighborhood clustering, directly addressing neighborhood differences in police activity. Minneapolis exhibits clear patterns of disproportionate policing by race, wherein Black people are searched at higher rates compared to White people. Temporal visualizations indicate that police stops declined following the murder of George Floyd. This analysis provides contemporary evidence on the state of policing for a major metropolitan area in the United States

The impact of antiretroviral therapy in a cohort of HIV infected patients going in and out of the San Francisco county jail.

BackgroundJails are an important venue of HIV care and a place for identification, treatment and referral for care. HIV infected inmates in the San Francisco County jail are offered antiretroviral treatment (ART), which many take only while in jail. We evaluated the effect of ART administration in a cohort of jail inmates going in and out of jail over a nine year period.Methodology/principal findingsIn this retrospective study, we examined inmates with HIV going in and out of jail. Inmates were categorized by patterns of ART use: continuous ART - ART both in and out of jail, intermittent ART - ART only in jail; never on ART - eligible by national guidelines, but refused ART. CD4 and HIV viral load (VL) were compared over time in these groups. Over a 9 year period, 512 inmates were studied: 388 (76%) on intermittent ART, 79 (15%) on continuous ART and 45(9%) never-on ART. In a linear mixed model analysis, inmates on intermittent ART were 1.43; 95%CI (1.03, 1.99) times and those never on ART were 2.89; 95%CI (1.71, 4.87) times more likely to have higher VL than inmates on continuous ART. Furthermore, Inmates on intermittent ART and never-on ART lost 1.60; 95%CI (1.06, 2.13) and 1.97; 95%CI (0.96, 3.00) more CD4 cells per month, respectively, compared to continuously treated inmates. The continuous ART inmates gained 0.67CD4 cells/month.Conclusions/significanceContinuous ART therapy in jail inmate's benefits CD4 cell counts and control of VL especially compared to those who never took ART. Although jail inmates on intermittent ART were more likely to lose CD4 cells and experience higher VL over time than those on continuous ART, CD4 cell loss was slower in these inmates as compared to inmates never on ART. Further studies are needed to evaluate whether or not intermittent ART provides some benefit in outcome if continuous ART is not possible or likely

Prenatal Exposure to Insecticides and Weight Trajectories Among South African Children in the VHEMBE Birth Cohort

BackgroundDichlorodiphenyltrichloroethane (DDT) or pyrethroid insecticides are sprayed inside dwellings for malaria vector control, resulting in high exposure to millions of people, including pregnant women. These chemicals disrupt endocrine function and may affect child growth. To our knowledge, few studies have investigated the potential impact of prenatal exposure to DDT or pyrethroids on growth trajectories.MethodsWe investigated associations between gestational insecticide exposure and child growth trajectories in the Venda Health Examination of Mothers, Babies and their Environment, a birth cohort of 751 children born between 2012 and 2013 in South Africa. Based on child weight measured at follow-up and abstracted from medical records, we modeled weight trajectories from birth to 5 years using SuperImposition, Translation and Rotation, which estimated two child-specific parameters: size (average weight) and tempo (age at peak weight velocity). We estimated associations between peripartum maternal concentrations of serum DDT, dichlorodiphenyldichloroethylene, or urinary pyrethroid metabolites and SuperImposition, Translation and Rotation parameters using marginal structural models.ResultsWe observed that a 10-fold increase in maternal concentrations of the pyrethroid metabolite trans-3-(2,2,-dicholorvinyl)-2,2-dimethyl-cyclopropane carboxylic acid was associated with a 21g (95% confidence interval = -40, -1.6) smaller size among boys but found no association among girls (Pinteraction = 0.07). Estimates suggested that pyrethroids may be associated with earlier tempo but were imprecise. We observed no association with serum DDT or dichlorodiphenyldichloroethylene.ConclusionsInverse associations between pyrethroids and weight trajectory parameters among boys are consistent with hypothesized disruption of androgen pathways and with our previous research in this population, and support the endocrine-disrupting potential of pyrethroids in humans